生成Diebold 和 Yilmaz（2014）的方差分解的matlab代码

以下是用 Matlab 生成 Diebold 和 Yilmaz（2014）的方差分解的代码示例：

% 假设有 m 个资产，每个资产有 n 个时间序列观测值
m = 10;
n = 100;

% 随机生成数据
data = randn(n, m);

% 计算每个资产的收益率
returns = diff(log(data));

% 计算协方差矩阵
covariance_matrix = cov(returns);

% 计算条件协方差矩阵
conditional_covariance_matrix = zeros(m, m, n-1);
for t = 2:n
    conditional_covariance_matrix(:, :, t-1) = cov(returns(1:t-1, :));
end

% 计算每个时间点的方差分解贡献率
forecast_error_variances = zeros(m, n-1);
for t = 2:n
    forecast_error_covariance = conditional_covariance_matrix(:, :, t-1) - ...
        covariance_matrix;
    for i = 1:m
        forecast_error_variances(i, t-1) = forecast_error_covariance(i, i);
    end
end
total_variance = sum(diag(covariance_matrix));
forecast_error_variances = forecast_error_variances ./ total_variance;

% 计算每个资产的方差分解贡献率
asset_variances = diag(covariance_matrix);
asset_volatilities = sqrt(asset_variances);
asset_forecast_error_variances = sum(forecast_error_variances, 2);
asset_forecast_error_volatilities = sqrt(asset_forecast_error_variances);
asset_total_variances = asset_variances + asset_forecast_error_variances;
asset_total_volatilities = sqrt(asset_total_variances);
asset_variance_decompositions = [asset_variances, ...
    asset_forecast_error_variances, asset_total_variances, ...
    asset_variances./asset_total_variances, ...
    asset_forecast_error_variances./asset_total_variances];
asset_volatility_decompositions = [asset_volatilities, ...
    asset_forecast_error_volatilities, asset_total_volatilities, ...
    asset_volatilities./asset_total_volatilities, ...
    asset_forecast_error_volatilities./asset_total_volatilities];

% 输出结果
fprintf('资产方差分解贡献率\n');
fprintf('%10s%20s%20s%20s%20s\n', '资产', '预测误差方差', '总方差', '比例', '预测误差比例');
for i = 1:m
    fprintf('%10d%20.2f%%%20.2f%%%20.2f%%%20.2f%%\n', i, ...
        100*asset_variance_decompositions(i, 2), ...
        100*asset_variance_decompositions(i, 3), ...
        100*asset_variance_decompositions(i, 4), ...
        100*asset_variance_decompositions(i, 5));
end

这段代码首先生成了一个大小为 n x m 的随机数据矩阵，然后计算了每个资产的收益率和收益率的协方差矩阵。接着，计算了每个时间点的条件协方差矩阵，并根据 Diebold 和 Yilmaz（2014）的方法，计算了每个时间点的方差分解贡献率。最后，计算了每个资产的方差分解贡献率和波动率分解贡献率，并输出结果。

需要注意的是，这段代码假设所有资产的收益率都是对数收益率，并且所有资产的收益率都是平稳的时间序列。如果收益率不是对数收益率或者不是平稳的时间序列，需要根据具体情况进行调整。